by Quentin Williams and Wessel Oosthuizen
We are now in the Age of With, when companies are harnessing the power of “with” to identify unique advantages through analytics and artificial intelligence. From mobile purchasing to same-day shipping to real-time problem resolution, the new normal in consumer interactions is speed with agility. And businesses everywhere have gotten the message. To meet heightened expectations and compete in unprecedented ways, many organisations are augmenting their human capabilities with digital technologies and advanced analytics. At Deloitte, all this shift the Age of With™: human expertise made greater with machines. It’s a new way of working, designed to automate high-volume tasks, glean deeper business insights, and free up humans to do things that add unique value.
To seize new opportunities—and fend off competitive threats—supply chain leaders need to spot and act upon a host of diverse, long-range issues. This requires the ability to gather and analyse voluminous data across end-to-end processes. Humans can’t do it alone in a timely manner, if at all. But by incorporating new technologies like artificial intelligence (AI) into their supply chain, they can, without breaking a sweat. Cutting costs remains the primary driver for digital and analytics investment, but improving the customer experience is becoming increasingly important as use cases show what’s possible.
Yet many companies underinvest in AI and related technologies across their supply chain. While 76 percent of respondents in the Deloitte 2019 Supply Chain and Digital Analytics Survey said developing digital and analytics capabilities was critical to their supply chain strategy, only 44 percent invest at least $5 million annually to develop these capabilities—even though nearly half the respondents expect an 11 to 20 percent return on their existing digital and analytics investments.
Part of the problem is the hype surrounding AI. If you expect to push a button and have the machine spit out insights, you’ll be disappointed. In reality, AI systems are only as good as the information they’re fed. To realise their promise, they need to receive current, accurate data from multiple source systems. As a related issue, some companies have difficulty articulating the full value proposition of digital and analytics initiatives. They don’t think big enough. They view technology through the prism of cost reduction or other small-scale goals, not as ways to transform their business.
What’s driving digital and analytics investment?
While digital and analytics can create value across the entire supply chain, a handful of applications has emerged as the most promising. These include inventory visibility optimisation, real-time manufacturing asset intelligence, and control tower-enabled visibility. This suggests companies are focusing on areas lower in complexity, but still struggling with advanced high-tech capabilities. When evaluating digital and analytics investments, business fit and cost are the most important factors. But don’t limit your thinking to what’s quick and easy. Balance your investments in areas that have a proven ROI with those targeting higher-risk, potentially higher-value opportunities.
New talent for a digital world
The skills needed to run a warehouse are very different from those required to build a warehouse management system. To do the former, you must know the business well. For the latter, business knowledge is the starting point. You also need to understand technology, develop a data model, and connect multiple data sources. This is but one example of why people with leading-edge digital and analytics skills are in short supply—and generally costly to hire.
In our survey, only 21 percent of companies rely most heavily on existing supply chain resources to execute digital and analytics initiatives. Another 82 percent identified internal expertise as their primary talent challenge. As a result, many companies are taking steps to build a more technically and analytically savvy supply chain workforce.
To bridge talent gaps in the short term, many companies build a digital and analytics ecosystem that includes external consultants and technology vendors. The company knows the business and technical details of a product or service; the external resources bring strategic benchmarks, programming skills, and statistical and analytical capabilities. Longer term, however, there’s no escaping the need for in-house talent, so knowledge transfer is critical to these partnerships. After all, it’s the internal resources who will need to manage the work once the digital tools and processes are in place.
What it takes to succeed
To make optimum use of AI and analytics tools, the supply chain workforce needs a blend of hard and soft skills. A thorough understanding of the business—and what drives its financials—remains as important as ever. But to deliver tech-enabled work, people also need curiosity, flexibility and a willingness to do things differently. To help your team get the experience it needs to excel in this new environment, be sure you’re exposing people to as broad a range of opportunities as you can. This might include the chance to work in different areas of supply chain, across different geographies, and with different technology platforms. And don’t overlook the insights people can gain spending time with customers. As the Age of With continues to transform traditional industries, the competition for technically and analytically savvy talent is high and will only increase in the years ahead. So be creative in your recruiting approaches and invest in continually developing your supply chain workforce.
Planning during uncertain times
The COVID-19 pandemic is a global crisis without modern parallel. The lack of precedent for a black swan event so broad in its impact across geographic, demographic, and economic sectors are causing demand- and supply disruption that influences and changes consumer behaviour dramatically on a daily level. Orchestrating the recovery from the COVID-19 pandemic will require unprecedented coordination and collaboration across organisations, markets, and the economy at large. While COVID-19 may be the catalyst for companies to revisit their global supply chain strategy and capabilities, short term actions need to be made to respond to the immediate challenge. Demand Orchestration is a Deloitte developed ensemble of analytic accelerators that will enable you to continuously generate the best possible response as new data becomes available.
The Demand Orchestration comprises of the following group of accelerators:
• Intuition: Advanced predictive analytics
• Integrated information collaboration
• Iterative Scenario optimisation
• Cognitive decision automation
Quentin Williams, Deloitte SA Associate Director in the Core Business Operations team
Wessel Oosthuizen, Deloitte SA AI Leader, Analytics